Identification of nuclides measured by gamma-ray spectrometry is typically performed by focusing on the peaks of an unknown spectrum, and in particular by curve-fitting the peaks to a library of known/predetermined spectral signatures. For example, peak energy assignments are made to nuclides (isotopes), and regression is used to compare pre-determined measurements or calculated spectra to an unknown. Using only peaks, however, can be limiting because information useful for identification may also be found elsewhere in the continuum of the full spectrum. For example, Compton scattering of some gamma-rays causes count data to spread beyond the peak locations to the continuum. This Compton scattered part of the gamma-ray spectrum contains information about the radioactive source and the gamma-ray detector. Furthermore, shielded sources frequently contain more counts in the continuum than in the peaks, making identification difficult. For example, peaks tend to vanish when shielding is thick.
Ideally therefore, all available information, including both peaks and continuum, would be used in a comprehensive analysis called “full spectrum analysis” to best determine the identity of an unknown source without requiring prior knowledge of any shielding. Automating full spectrum analysis to run unattended on a computer, however, has been a challenge because of both a limited ability to model or measure all the relevant physics for all possible sources and shields, and the processor intensive nature of comparing an unknown spectrum against all entries in a given spectral library.
A typical example of full spectrum analysis and gamma-ray signature identification in the prior art involves first creating a library of possible signatures, such as by measuring known signatures, calculating known signatures in real time, or pre-calculating known signatures for a catalog. Multiple regression is then performed to search for the most similar match of an unknown to a known in the library, where the test for similarity may be a test for maximum likelihood, chi-square or simple differences. Regression test comparison involves solving a linear series of equations, usually reduced to array algebra, typically where a matrix is inverted. Matrix inversion can become unreliable as library elements become similar. And since computer time is proportional to the number of channels (energy increments) in the gamma-ray spectrum times the number of library elements, providing a large library can significantly increase processing time/computing resources. For example, 8000 library elements times 200 channels is 1.6 million operations. Furthermore, fine tuning the library at regression time is another option known in the art, where the calculation codes generate new variations as the regression is running. This approach, however, can take even longer since computational time is proportional to the number of channels in the spectrum times the number of library elements times the number of seconds to calculate a spectral variation.
Since the possible list of predetermined known entries for which variations must be calculated is typically in the hundreds, and not the typical range of up to about thirty predetermined known entries in the library that exist in commercial algorithms, this pushes a typical multiple regression or regression/model approach to take up to hours of CPU time, especially if geometry variational calculations are involved. Because of these limitations, traditional regression approaches typically have limited library sizes in order to reduce the total number of regressions performed and keep CPU time reasonable. The disadvantage of small libraries, however, is that they induce classically systematic errors leading to incorrect library lookup. As a consequence, the results of such an identification scheme using a limited library may not provide a complete or accurate characterization or identification of the unknown spectrum.
What is needed therefore is a real-time (i.e. on the order of a second or less) method and system for identifying gamma-ray signatures that uses little computer processor time and resources, analyzes the full gamma-ray spectrum, and can be adjusted to address numerous identification objectives, such as for example nuclide ID, source strength, age, shielding thickness, or nuclear material form. In particular, what is needed is a method and system that effectively transforms and reduces the voluminous spectral data contained in a comprehensive spectral library to a small, manageable representation/signature(s) of the library, and directly compares an unknown spectrum against the representation(s) to find a match or at least determine a characterization of the unknown by similarity to all spectral entries of the library. Moreover, this is performed without the computational burden of having to perform fine-grain multiple regression, i.e. fitting the unknown against each entry of the source library. Additionally, and with respect to building the library, such a method and system would also be configured to obtain factual information/details about the signatures used as library entries, without having to particularly measure for the information. While such information is typically obtained by measurement, it would be advantageous to instead use suitably accurate simulations of the known/predetermined signatures to obtain this information and thereby avoid the expense (in terms of processor time/computing resources) associated with actually measuring for all possible variations on the signatures when building a comprehensive library.